Knowledge about the seismic elastic modulus dispersion,and associated attenuation,in fluid-saturated rocks is essential for better interpretation of seismic observations taken as part of hydrocarbon identification and...Knowledge about the seismic elastic modulus dispersion,and associated attenuation,in fluid-saturated rocks is essential for better interpretation of seismic observations taken as part of hydrocarbon identification and time-lapse seismic surveillance of both conventional and unconventional reservoir and overburden performances.A Seismic Elastic Moduli Module has been developed,based on the forced-oscillations method,to experimentally investigate the frequency dependence of Young's modulus and Poisson's ratio,as well as the inferred attenuation,of cylindrical samples under different confining pressure conditions.Calibration with three standard samples showed that the measured elastic moduli were consistent with the published data,indicating that the new apparatus can operate reliably over a wide frequency range of f∈[1-2000,10^(6)]Hz.The Young's modulus and Poisson's ratio of the shale and the tight sandstone samples were measured under axial stress oscillations to assess the frequency-and pressure-dependent effects.Under dry condition,both samples appear to be nearly frequency independent,with weak pressure dependence for the shale and significant pressure dependence for the sandstone.In particular,it was found that the tight sandstone with complex pore microstructure exhibited apparent dispersion and attenuation under brine or glycerin saturation conditions,the levels of which were strongly influenced by the increased effective pressure.In addition,the measured Young's moduli results were compared with the theoretical predictions from a scaled poroelastic model with a reasonably good agreement,revealing that the combined fluid flow mechanisms at both mesoscopic and microscopic scales possibly responsible for the measured dispersion.展开更多
Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep l...Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based methods.In order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets.Three kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training process.Benefit from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real data.Moreover,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model.The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases.And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.展开更多
This paper proposes a voltage-based hot-spot detection method for defective cells in PV module using projector. The presence of internal crystal defects is one of the main causes of hot-spot phenomenon in PV modules. ...This paper proposes a voltage-based hot-spot detection method for defective cells in PV module using projector. The presence of internal crystal defects is one of the main causes of hot-spot phenomenon in PV modules. Authors previously investigated the physical characteristics of hot-spot phenomenon referring to internal crystal defect. Based on it, a hot-spot detection method named as current-based SRC （self reverse current） detection method is developed. However, it becomes extraordinarily complicated to determine the defective cells under low illumination. In order to avoid this disadvantage, authors improve the SRC detection method by applying voltage. From the feasibility experiment results, it is confirmed that by calculating cell HSI （hotspots index） with voltage, the PV modules with defective cells can be prospectively excluded even under low illumination.展开更多
The conventional P ＆ O （perturb-and-observe） method, which is the most widely used as MPPT （maximum power point tracking） control, has the problem of low efficiency and unstable operation when solar radiation cha...The conventional P ＆ O （perturb-and-observe） method, which is the most widely used as MPPT （maximum power point tracking） control, has the problem of low efficiency and unstable operation when solar radiation changes drastically. Aiming at this problem, this paper improves the conventional P ＆ O method to reduce the bad effect of solar radiation by shortening the sampling interval of PV module＇s output power while keeping the operating period unchanged. Experiments are conducted to study efficiency gains of improved method when solar radiation changes drastically. The result shows that, by this method, the efficiency of MPPT control can be increased 17% in average when PV module simulator is used and 20% at maximum when actual PV module is used, compared with the conventional P ＆ O method.展开更多
Nowadays, in a household PV （photovoltaic） generation system, it is generally connecting PV modules in series and then output to the power-conditioner. However, when PV modules are mismatched, it will lead to a wron...Nowadays, in a household PV （photovoltaic） generation system, it is generally connecting PV modules in series and then output to the power-conditioner. However, when PV modules are mismatched, it will lead to a wrong MPPT （maximum power point tracking） to all modules and a power decreasing of the whole system. Aiming at this problem, this paper presents the idea which improves the MPPT without changing the conventional power-conditioner, by adding a Buck type DC-DC （direct current） converter behind each module. Simulations of PSIM （power simulation） and experiments are taken to prove this theory. The result shows that, by this idea, the generated power of the conventional PV generation system can be greatly increased under the condition of mismatch.展开更多
Highly sensitive methods are important for monitoring the concentration of metal ions in industrial wastewater.Here,we developed a new probe for the determination of metal ions by fluorescence quenching.The probe cons...Highly sensitive methods are important for monitoring the concentration of metal ions in industrial wastewater.Here,we developed a new probe for the determination of metal ions by fluorescence quenching.The probe consists of hydroxylated graphene quantum dots(H-GQDs),prepared from GQDs by electrochemical method followed by surface hydroxylation.It is a non-reactive indicator with high sensitivity and detection limits of 0.01μM for Cu2+,0.005μM for Al3+,0.04μM for Fe3+,and 0.02μM for Cr3+.In addition,the low biotoxicity and excellent solubility of H-GQDs make them promising for application in wastewater metal ion detection.展开更多
As a novel zero-dimensional(0D)material,metal carbides and/or carbonitrides(MXenes)quantum dots(MQDs)show unique photoluminescence properties and excellent biocompatibility.However,due to the limited synthesis methods...As a novel zero-dimensional(0D)material,metal carbides and/or carbonitrides(MXenes)quantum dots(MQDs)show unique photoluminescence properties and excellent biocompatibility.However,due to the limited synthesis methods and research to date,many new features have yet to be uncovered.Here,to explore their new properties and expand biological applications,chlorine and nitrogen co-doped Ti_(3)C_(2)MXene quantum dots(Cl,N-Ti_(3)C_(2)MQDs)were designed and synthesized,and their hydroxyl radical scavenging properties were investigated for the first time,revealing outstanding performance.Cl,N-Ti_(3)C_(2)MQDs was directly stripped from bulk Ti_(3)Al C_(2)by electrochemical etching,while N and Cl are successfully introduced to carbon skeleton and Ti boundaries in the etching process by electrochemical reactions between selected electrolytes and Ti_(3)C_(2)skeleton,respectively.The obtained Cl,N-Ti_(3)C_(2)MQDs exhibit large surface-to-volume ratio due to small particle size(ca.3.45 nm)and excellent higher scavenging activity(93.3%)and lower usage(12.5μg/m L)towards hydroxyl radicals than the previous reported graphene-based nanoparticles.The underlying mechanism of scavenging activity was also studied based on the reduction experiment with potassium permanganate(KMnO_(4)).The reducing ability of the intrinsic Ti_(3)C_(2)structure and electron donation of double dopants are the main contributors to the outstanding scavenging activity.展开更多
Most deep learning methods in hyperspectral image(HSI)classification use local learning methods,where overlapping areas between pixels can lead to spatial redundancy and higher computational cost.This paper proposes a...Most deep learning methods in hyperspectral image(HSI)classification use local learning methods,where overlapping areas between pixels can lead to spatial redundancy and higher computational cost.This paper proposes an efficient global learning(EGL)framework for HSI classification.The EGL framework was composed of universal global random stratification(UGSS)sampling strategy and a classification model BrsNet.The UGSS sampling strategy was used to solve the problem of insufficient gradient variance resulted from limited training samples.To fully extract and explore the most distinguishing feature representation,we used the modified linear bottleneck structure with spectral attention as a part of the BrsNet network to extract spectral spatial information.As a type of spectral attention,the shuffle spectral attention module screened important spectral features from the rich spectral information of HSI to improve the classification accuracy of the model.Meanwhile,we also designed a double branch structure in BrsNet that extracted more abundant spatial information from local and global perspectives to increase the performance of our classification framework.Experiments were conducted on three famous datasets,IP,PU,and SA.Compared with other classification methods,our proposed method produced competitive results in training time,while having a greater advantage in test time.展开更多
基金The authors would like to acknowledge financial support from NSFC Basic Research Program on Deep Petroleum Resource Accumulation and Key Engineering Technologies(U19B6003-04-03)National Natural Science Foundation of China(41930425)+2 种基金Beijing Natural Science Foundation(8222073),R&D Department of China National Petroleum Corporation(Investigations on fundamental experiments and advanced theoretical methods in geophysical prospecting applications,2022DQ0604-01)Scientific Research and Technology Development Project of PetroChina(2021DJ1206)National Key Research and Development Program of China(2018YFA0702504).
文摘Knowledge about the seismic elastic modulus dispersion,and associated attenuation,in fluid-saturated rocks is essential for better interpretation of seismic observations taken as part of hydrocarbon identification and time-lapse seismic surveillance of both conventional and unconventional reservoir and overburden performances.A Seismic Elastic Moduli Module has been developed,based on the forced-oscillations method,to experimentally investigate the frequency dependence of Young's modulus and Poisson's ratio,as well as the inferred attenuation,of cylindrical samples under different confining pressure conditions.Calibration with three standard samples showed that the measured elastic moduli were consistent with the published data,indicating that the new apparatus can operate reliably over a wide frequency range of f∈[1-2000,10^(6)]Hz.The Young's modulus and Poisson's ratio of the shale and the tight sandstone samples were measured under axial stress oscillations to assess the frequency-and pressure-dependent effects.Under dry condition,both samples appear to be nearly frequency independent,with weak pressure dependence for the shale and significant pressure dependence for the sandstone.In particular,it was found that the tight sandstone with complex pore microstructure exhibited apparent dispersion and attenuation under brine or glycerin saturation conditions,the levels of which were strongly influenced by the increased effective pressure.In addition,the measured Young's moduli results were compared with the theoretical predictions from a scaled poroelastic model with a reasonably good agreement,revealing that the combined fluid flow mechanisms at both mesoscopic and microscopic scales possibly responsible for the measured dispersion.
基金financially supported by the NSFC(Grant No.41974126 and 41674116)the National Key Research and Development Program of China(Grant No.2018YFA0702501)the 13th 5-Year Basic Research Program of China National Petroleum Corporation(CNPC)(2018A-3306)。
文摘Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based methods.In order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets.Three kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training process.Benefit from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real data.Moreover,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model.The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases.And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.
文摘This paper proposes a voltage-based hot-spot detection method for defective cells in PV module using projector. The presence of internal crystal defects is one of the main causes of hot-spot phenomenon in PV modules. Authors previously investigated the physical characteristics of hot-spot phenomenon referring to internal crystal defect. Based on it, a hot-spot detection method named as current-based SRC （self reverse current） detection method is developed. However, it becomes extraordinarily complicated to determine the defective cells under low illumination. In order to avoid this disadvantage, authors improve the SRC detection method by applying voltage. From the feasibility experiment results, it is confirmed that by calculating cell HSI （hotspots index） with voltage, the PV modules with defective cells can be prospectively excluded even under low illumination.
文摘The conventional P ＆ O （perturb-and-observe） method, which is the most widely used as MPPT （maximum power point tracking） control, has the problem of low efficiency and unstable operation when solar radiation changes drastically. Aiming at this problem, this paper improves the conventional P ＆ O method to reduce the bad effect of solar radiation by shortening the sampling interval of PV module＇s output power while keeping the operating period unchanged. Experiments are conducted to study efficiency gains of improved method when solar radiation changes drastically. The result shows that, by this method, the efficiency of MPPT control can be increased 17% in average when PV module simulator is used and 20% at maximum when actual PV module is used, compared with the conventional P ＆ O method.
文摘Nowadays, in a household PV （photovoltaic） generation system, it is generally connecting PV modules in series and then output to the power-conditioner. However, when PV modules are mismatched, it will lead to a wrong MPPT （maximum power point tracking） to all modules and a power decreasing of the whole system. Aiming at this problem, this paper presents the idea which improves the MPPT without changing the conventional power-conditioner, by adding a Buck type DC-DC （direct current） converter behind each module. Simulations of PSIM （power simulation） and experiments are taken to prove this theory. The result shows that, by this idea, the generated power of the conventional PV generation system can be greatly increased under the condition of mismatch.
基金financially supported by the National Natural Science Foundation of China (No. 21674011)Beijing Municipal Natural Science Foundation (No. 2172040)
文摘Highly sensitive methods are important for monitoring the concentration of metal ions in industrial wastewater.Here,we developed a new probe for the determination of metal ions by fluorescence quenching.The probe consists of hydroxylated graphene quantum dots(H-GQDs),prepared from GQDs by electrochemical method followed by surface hydroxylation.It is a non-reactive indicator with high sensitivity and detection limits of 0.01μM for Cu2+,0.005μM for Al3+,0.04μM for Fe3+,and 0.02μM for Cr3+.In addition,the low biotoxicity and excellent solubility of H-GQDs make them promising for application in wastewater metal ion detection.
基金National Natural Science Foundation of China(Grant No.21674011,21404008)Beijing Municipal Natural Science Foundation(Grant No.2172040)+1 种基金Beijing Organization department outstanding talented person project(2013D009006000001)the Fundamental Research Funds for the Central Universities(FRF-GF-17-B11)。
文摘As a novel zero-dimensional(0D)material,metal carbides and/or carbonitrides(MXenes)quantum dots(MQDs)show unique photoluminescence properties and excellent biocompatibility.However,due to the limited synthesis methods and research to date,many new features have yet to be uncovered.Here,to explore their new properties and expand biological applications,chlorine and nitrogen co-doped Ti_(3)C_(2)MXene quantum dots(Cl,N-Ti_(3)C_(2)MQDs)were designed and synthesized,and their hydroxyl radical scavenging properties were investigated for the first time,revealing outstanding performance.Cl,N-Ti_(3)C_(2)MQDs was directly stripped from bulk Ti_(3)Al C_(2)by electrochemical etching,while N and Cl are successfully introduced to carbon skeleton and Ti boundaries in the etching process by electrochemical reactions between selected electrolytes and Ti_(3)C_(2)skeleton,respectively.The obtained Cl,N-Ti_(3)C_(2)MQDs exhibit large surface-to-volume ratio due to small particle size(ca.3.45 nm)and excellent higher scavenging activity(93.3%)and lower usage(12.5μg/m L)towards hydroxyl radicals than the previous reported graphene-based nanoparticles.The underlying mechanism of scavenging activity was also studied based on the reduction experiment with potassium permanganate(KMnO_(4)).The reducing ability of the intrinsic Ti_(3)C_(2)structure and electron donation of double dopants are the main contributors to the outstanding scavenging activity.
基金funded by National Natural Special Foundation of Central Government to Guide Local Science&Technology Development(2021Szvup032).
文摘Most deep learning methods in hyperspectral image(HSI)classification use local learning methods,where overlapping areas between pixels can lead to spatial redundancy and higher computational cost.This paper proposes an efficient global learning(EGL)framework for HSI classification.The EGL framework was composed of universal global random stratification(UGSS)sampling strategy and a classification model BrsNet.The UGSS sampling strategy was used to solve the problem of insufficient gradient variance resulted from limited training samples.To fully extract and explore the most distinguishing feature representation,we used the modified linear bottleneck structure with spectral attention as a part of the BrsNet network to extract spectral spatial information.As a type of spectral attention,the shuffle spectral attention module screened important spectral features from the rich spectral information of HSI to improve the classification accuracy of the model.Meanwhile,we also designed a double branch structure in BrsNet that extracted more abundant spatial information from local and global perspectives to increase the performance of our classification framework.Experiments were conducted on three famous datasets,IP,PU,and SA.Compared with other classification methods,our proposed method produced competitive results in training time,while having a greater advantage in test time.
